Numerical control gantry milling machine machining accuracy prediction method, system and computer

Abstract

The present invention relates to the technical field of equipment data processing, and provides a method, a system and a computer for predicting the machining accuracy of a numerically controlled gantry milling machine. The method for predicting the machining accuracy of a numerically controlled gantry milling machine includes: setting a plurality of initial temperature measurement points and constructing a temperature measurement curve; collecting a plurality of real-time displacements of the electric spindle and constructing an error curve; constructing a plurality of to-be-determined temperature measurement point groups based on a plurality of temperature measurement point groups and calculating a plurality of thermal key point coefficients; determining a thermosensitive temperature measurement point group; constructing an electric spindle state model based on historical monitoring data; calculating a basic prediction error based on a plurality of updated temperature values; obtaining a degradation prediction error based on updated monitoring data, and obtaining a final prediction error based on the basic prediction error and the degradation prediction error. By screening out the thermosensitive temperature measurement point group, more accurate and efficient thermal error prediction is realized, and combined with the prediction of the degradation state of the electric spindle, the final machining accuracy prediction is more comprehensive and accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment data processing, and particularly to a method, a system and a computer for predicting the machining accuracy of a numerically controlled gantry milling machine. Background Art

[0002] With the development of technology, numerically controlled machine tools are more and more widely used. Among them, the gantry milling machine is an important type in the machine tool family.

[0003] The numerically controlled gantry milling machine has a complex structure, and its components involve various fields such as machinery, hydraulics, electricity and electronics. In the working state, the working conditions are relatively complex. Therefore, the machining accuracy of the numerically controlled gantry milling machine is affected by various factors, and the control of machining accuracy is the key to production quality.

[0004] In the prior art, the analysis of the machining accuracy of the numerically controlled gantry milling machine is usually based on geometric error and thermal error, and the accuracy of the thermal error prediction method still needs to be improved. And for the numerically controlled gantry milling machine that operates continuously for a long time, the electric spindle has a degradation process. The electric spindle in the degraded state is not stable enough during the milling process, and is prone to radial jump, resulting in an increase in machining error. Therefore, both the thermal error prediction and the uncertainty of the electric spindle state lead to insufficient prediction accuracy of the machining accuracy of the numerically controlled gantry milling machine. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method, a system and a computer for predicting the machining accuracy of a numerically controlled gantry milling machine. The present invention tests a large number of initial temperature measurement points, groups them and calculates the thermal key point coefficients, screens out several thermosensitive temperature measurement points, improves the prediction accuracy of thermal error, and can improve the temperature monitoring efficiency. A state model of the electric spindle is constructed, and the error caused by the degradation of the electric spindle is predicted through the state of the electric spindle, so as to realize a more comprehensive and accurate prediction of the machining accuracy of the numerically controlled gantry milling machine. The present invention aims to solve the technical problem of insufficient prediction accuracy of the machining accuracy of the numerically controlled gantry milling machine in the prior art.

[0006] In order to achieve the above purpose, the present invention is realized by the following technical solutions:

[0007] A method for predicting the machining accuracy of a numerically controlled gantry milling machine includes the following steps:

[0008] Set a number of initial temperature measurement points on the milling machine, run the milling machine to the thermal equilibrium state, collect a number of initial temperature values through the initial temperature measurement points, and construct a temperature measurement curve based on the initial temperature values and the corresponding time;

[0009] Collect a number of real-time displacements of the electric spindle through a sensor to obtain test error values, and construct an error curve based on the time corresponding to the real-time displacements and the test error values;

[0010] Based on the similarity between several of the temperature measurement curves, several of the initial temperature measurement points are divided into several temperature measurement point groups. Based on several of the temperature measurement point groups, several to-be-determined temperature measurement point groups are constructed, and several thermal key point coefficients corresponding to several of the to-be-determined temperature measurement point groups are calculated;

[0011] Compare several of the thermal key point coefficients, and establish the to-be-determined temperature measurement point group corresponding to the largest of the thermal key point coefficients as the thermal-sensitive temperature measurement point group, where the thermal-sensitive temperature measurement point group includes several thermal-sensitive temperature measurement points;

[0012] Obtain the historical monitoring data of the motorized spindle, and construct a motorized spindle state model based on the historical monitoring data;

[0013] Obtain several updated temperature values from several of the thermal-sensitive temperature measurement points, and calculate a basic prediction error based on several of the updated temperature values;

[0014] Obtain the updated monitoring data of the motorized spindle, obtain a degradation prediction error based on the updated monitoring data and the motorized spindle state model, and obtain a final prediction error based on the basic prediction error and the degradation prediction error.

[0015] Furthermore, the step of constructing several to-be-determined temperature measurement point groups based on several of the temperature measurement point groups includes:

[0016] Calculate the temperature average value of all of the initial temperature values in the temperature measurement point group, select the maximum average value from several of the temperature average values, establish the temperature measurement point group corresponding to the maximum average value as the first temperature measurement point group, and establish the remaining several temperature measurement point groups as several second temperature measurement point groups;

[0017] Recombine several of the initial temperature measurement points in several of the second temperature measurement point groups into several transitional temperature measurement point groups;

[0018] The first temperature measurement point group and the transitional temperature measurement point group form a to-be-determined temperature measurement point group.

[0019] Even further, the to-be-determined temperature measurement point group includes several to-be-determined temperature measurement points, and the step of calculating several thermal key point coefficients corresponding to several of the to-be-determined temperature measurement point groups includes:

[0020] Obtain several of the initial temperature values at the same moment as the test error value through several of the to-be-determined temperature measurement points, and establish them as several to-be-regressed temperature values, perform linear regression on several of the to-be-regressed temperature values to obtain a prediction error value, and several of the to-be-determined temperature measurement point groups correspond to several of the prediction error values;

[0021] Calculate the thermal key point coefficient corresponding to the temperature measurement point group to be determined based on the several test error values, the temperature measurement point group to be determined, and the several prediction error values corresponding to the temperature measurement point group to be determined.

[0022] Furthermore, the formula for the thermal key point coefficient is:

[0023]

[0024] Where, represents the thermal key point coefficient, represents the number of test error values, represents the number of temperature measurement points to be determined in the temperature measurement point group to be determined, , represents the th test error value, represents the prediction error value corresponding to the th test error value, represents the average value of the several test error values.

[0025] Furthermore, the historical monitoring data includes the historical radial runout of the motorized spindle and the historical vibration signals of the front and rear bearings.

[0026] Furthermore, the formula for the motorized spindle state model is:

[0027]

[0028] Where, represents the state of the motorized spindle at time, represents the state of the motorized spindle at time, represents the diffusion coefficient, represents the standard Brownian motion in the healthy stage of the motorized spindle, represents the initial time when the motorized spindle starts to work, represents the time when the motorized spindle transitions from the healthy stage to the slow degradation stage, represents the state of the motorized spindle at time, represents the drift coefficient, represents the standard Brownian motion in the slow degradation stage of the motorized spindle, represents the time when the motorized spindle transitions from the slow degradation stage to the rapid degradation stage, represents the state of the motorized spindle at time, represents the standard Brownian motion in the rapid degradation stage of the motorized spindle.

[0029] Further, the step of calculating the basic prediction error based on a plurality of the updated temperature values includes:

[0030] Based on a plurality of the temperature measurement curves and the error curves corresponding to the thermosensitive temperature measurement points, taking a plurality of the initial temperature values as independent variables and the test error value as a dependent variable, performing multiple linear regression to obtain an error linear regression model;

[0031] Input a plurality of the updated temperature values into the error linear regression model to obtain the basic prediction error.

[0032] Furthermore, the step of obtaining the degradation prediction error based on the updated monitoring data and the electric spindle state model includes:

[0033] Perform Kalman filtering on the updated monitoring data to obtain the updated state of the electric spindle;

[0034] Based on the updated state of the electric spindle and the electric spindle state model, obtain the degradation prediction error.

[0035] A numerical control gantry milling machine processing accuracy prediction system, which is applied to the numerical control gantry milling machine processing accuracy prediction method as described in the above technical solution, and the system includes:

[0036] A first test module, configured to set a plurality of initial temperature measurement points on the milling machine, run the milling machine to a thermal equilibrium state, collect a plurality of initial temperature values through the initial temperature measurement points, and construct a temperature measurement curve based on the initial temperature values and the corresponding time;

[0037] A second test module, configured to collect a plurality of real-time displacements of the electric spindle through a sensor to obtain a test error value, and construct an error curve based on the time corresponding to the real-time displacement and the test error value;

[0038] A determination module, configured to divide a plurality of the initial temperature measurement points into a plurality of temperature measurement point groups based on the similarity between a plurality of the temperature measurement curves, construct a plurality of to-be-determined temperature measurement point groups based on the plurality of temperature measurement point groups, and calculate a plurality of thermal key point coefficients corresponding to the plurality of to-be-determined temperature measurement point groups;

[0039] An establishment module, configured to compare a plurality of the thermal key point coefficients, and establish the to-be-determined temperature measurement point group corresponding to the largest thermal key point coefficient as a thermosensitive temperature measurement point group, where the thermosensitive temperature measurement point group includes a plurality of thermosensitive temperature measurement points;

[0040] A construction module, configured to obtain the historical monitoring data of the electric spindle, and construct an electric spindle state model based on the historical monitoring data;

[0041] The first prediction module is used to obtain a plurality of updated temperature values from a plurality of the thermal temperature measurement points, and calculate a basic prediction error based on the plurality of updated temperature values.

[0042] The second prediction module is used to obtain updated monitoring data of the electric spindle, obtain a degradation prediction error based on the updated monitoring data and the electric spindle state model, and obtain a final prediction error based on the basic prediction error and the degradation prediction error.

[0043] A computer includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for predicting the machining accuracy of a numerically controlled gantry milling machine as described in the above technical solution.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: By setting a plurality of the initial temperature measurement points and running the milling machine for testing, collecting test data, constructing a plurality of the temperature measurement curves, classifying the temperature measurement curves with similarity into the same group, that is, dividing a plurality of the initial temperature measurement points into a plurality of the temperature measurement point groups, selecting a plurality of the initial temperature measurement points from the plurality of the temperature measurement point groups for recombination of various combinations, that is, forming a plurality of the to-be-determined temperature measurement point groups, and respectively selecting the optimal thermosensitive temperature measurement point combination according to the thermal key point coefficient, the temperature change of a plurality of the thermosensitive temperature measurement points in the thermosensitive temperature measurement point group has the highest correlation with the deformation of the electric spindle caused by temperature. The thermal error is predicted through the data of the thermosensitive temperature measurement points, greatly improving the accuracy of thermal error prediction, and the total number of the thermosensitive temperature measurement points is greatly reduced compared with the total number of the initial temperature measurement points. Only the data of the thermosensitive temperature measurement points are monitored during the working state of the milling machine, saving costs and having high efficiency while ensuring the accuracy of thermal error prediction; By constructing the electric spindle state model, the state of the electric spindle on the milling machine running for a long time in production is analyzed. The electric spindle ages and degrades over time, and its state change will bring a certain degradation error, which is likely to cause radial jump during the machining process. Based on the updated monitoring data, the current state of the electric spindle is identified, and the future state and existing degradation errors can be predicted, and finally combined with the predicted thermal error to form a more accurate and comprehensive machining accuracy prediction. Description of the Drawings

[0045] Figure 1 It is a flowchart of the method for predicting the machining accuracy of a numerically controlled gantry milling machine in the first embodiment of the present invention;

[0046] Figure 2 It is a structural block diagram of the system for predicting the machining accuracy of a numerically controlled gantry milling machine in the second embodiment of the present invention;

[0047] The following specific embodiments will further illustrate the present invention in conjunction with the above-mentioned drawings. Specific Embodiments

[0048] To facilitate the understanding of the present invention, the present invention will be described more comprehensively below with reference to the relevant drawings. Several embodiments of the present invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided to make the disclosure of the present invention more thorough and comprehensive.

[0049] It should be noted that when an element is referred to as being "fixedly provided on" another element, it can be directly on the other element or there may also be an intermediate element. When an element is considered to be "connected" to another element, it can be directly connected to the other element or there may be an intermediate element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0050] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs. The terms used in the description of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more of the related listed items.

[0051] Please refer to Figure 1 , the numerical control gantry milling machine machining accuracy prediction method in the first embodiment of the present invention includes the following steps:

[0052] Step S10: Set a plurality of initial temperature measurement points on the milling machine, run the milling machine to the thermal equilibrium state, collect a plurality of initial temperature values through the initial temperature measurement points, and construct a temperature measurement curve based on the initial temperature values and the corresponding time;

[0053] The geometric errors of a numerically controlled gantry milling machine can be measured by a laser interferometer, and the geometric errors of the milling machine do not change with temperature and can be obtained based on the factory data of each model of milling machine. During the working process, the components of the milling machine expand and contract due to temperature changes. Although it is difficult to identify with the naked eye, it will have a greater impact on the machining accuracy of the numerically controlled gantry milling machine. The influence degree of the heat source on the spindle of the numerically controlled gantry milling machine in the working state is greater than that of the XYZ-axis workbench and other machine tool components of the numerically controlled gantry milling machine, and the resulting thermal deformation is greater. The machining error caused by the thermal deformation of the motorized spindle accounts for a very large proportion in the overall thermal error of the machine tool. Preferably, in this embodiment, a PT100 temperature sensor is used, and the initial temperature measurement points are arranged at positions such as on the spindle box, spindle base, spindle coolant outlet, spindle coolant inlet, left end of the lower spindle bearing, right end of the lower spindle bearing, spindle motor, upper spindle bearing, front side of the milling machine bed, rear side of the milling machine bed, contact position between the guide rail and the bed, and external environment temperature. During the process of the milling machine running to the thermal equilibrium state, the real working conditions are simulated. When the temperatures of all the initial temperature measurement points do not increase significantly, it means that the milling machine reaches the thermal equilibrium state, and the temperature change curves of all the initial temperature measurement points are constructed.

[0054] Step S20: Collect a number of real-time displacements of the motorized spindle through sensors to obtain test error values, and construct an error curve based on the time corresponding to the real-time displacements and the test error values;

[0055] Preferably, the real-time displacements of the motorized spindle are measured by the five-point method through displacement sensors. Specifically, two displacement sensors are arranged in the X-axis direction of the motorized spindle, two displacement sensors are arranged in the Y-axis direction of the motorized spindle, a steel inspection bar is set on the motorized spindle, and one displacement sensor is arranged in the Z-axis direction of the steel inspection bar to measure the thermal elongation error of the motorized spindle. The initial temperature value and the test error value are both collected at the same time interval. By converting the obtained number of real-time displacements, the rotational error of the motorized spindle around the X-axis, the rotational error around the Y-axis, and the thermal elongation error can be obtained. It can be understood that at the same moment, one test error value corresponds to the temperatures of all the initial temperature measurement points at that moment.

[0056] Step S30: Based on the similarity between a number of the temperature change curves, divide a number of the initial temperature measurement points into a number of temperature measurement point groups, construct a number of to-be-determined temperature measurement point groups based on the number of the temperature measurement point groups, and calculate a number of thermal key point coefficients corresponding to the number of the to-be-determined temperature measurement point groups;

[0057] Preferably, the initial temperature measurement points with similar temperature change laws can be determined according to the shape of the temperature measurement curve, or the K-means algorithm can be used to perform cluster analysis on the sample data of all the initial temperature measurement points, so as to group them. Specifically, in this embodiment, the cluster analysis method is adopted; to predict the thermal error, data at the thermal sensitive points need to be collected. The thermal sensitive points are the key temperature measurement points with the highest degree of correlation with the thermal deformation of the electric spindle. There are usually multiple thermal sensitive points. To select the optimal thermal sensitive points, the temperature measurement points with the greatest influence on the thermal error need to be selected from multiple similar initial temperature measurement points in the temperature measurement point group.

[0058] The step S30 includes:

[0059] S310: Calculate the temperature average value of all the initial temperature values in the temperature measurement point group, select the maximum average value from several of the temperature average values, establish the temperature measurement point group corresponding to the maximum average value as the first temperature measurement point group, and establish the remaining several temperature measurement point groups as several second temperature measurement point groups;

[0060] Preferably, since the electric spindle follows the principle of thermal expansion and contraction, in the temperature measurement point group with the most significant overall temperature increase, all temperature measurement points are established as thermal sensitive points, that is, all the initial temperature measurement points in the first temperature measurement point group are thermal sensitive points.

[0061] S320: Recombine several of the initial temperature measurement points in the several second temperature measurement point groups into several transition temperature measurement point groups;

[0062] S330: The first temperature measurement point group and the transition temperature measurement point group form a temperature measurement point group to be determined;

[0063] Preferably, the specific method for forming the temperature measurement point group to be determined is exemplified as follows: several of the temperature measurement point groups are specifically three groups, namely A, B, and C. Group A includes two initial temperature measurement points, T1 and T2; Group B includes two initial temperature measurement points, T3 and T4; Group C includes three initial temperature measurement points, T5, T6, and T7. If the average temperature rise of Group C is the highest among the three groups in the temperature measurement result, then Group C is directly established as the first temperature measurement point group, and the three initial temperature measurement points, T5, T6, and T7, are directly established as the thermal sensitive points. Both Group A and Group B are established as the second temperature measurement point groups. One initial temperature measurement point is selected from each of Group A and Group B, and multiple transition temperature measurement point groups are re - formed through permutation and combination. In this example, specifically four transition temperature measurement point groups are re - formed, namely: T1, T3 group; T1, T4 group; T2, T3 group; and T2, T4 group. The four transition temperature measurement point groups and the first temperature measurement point group form four temperature measurement point groups to be determined, namely: T1, T3, T5, T6, T7 group; T1, T4, T5, T6, T7 group; T2, T3, T5, T6, T7 group; T2, T4, T5, T6, T7 group.

[0064] The temperature measurement point group to be determined includes several temperature measurement points to be determined, and step S30 further includes:

[0065] S340: Obtain several initial temperature values at the same moment as the test error value through several of the temperature measurement points to be determined, and establish them as several temperature values to be regressed. Perform linear regression on the several temperature values to be regressed to obtain a prediction error value. The temperature measurement point group to be determined corresponds to several of the prediction error values;

[0066] Preferably, for example, when the milling machine runs for one hour, the five initial temperature values corresponding to the five temperature measurement points to be determined in the T1, T3, T5, T6, T7 group at this moment are all established as the temperature values to be regressed. Based on the curves of the five temperature measurement points to be determined and the error curve for fitting, several regression coefficients and correction coefficients of the linear regression equation are obtained. According to the linear regression equation, the prediction error value when the milling machine runs for one hour is calculated. The total number of the prediction error values corresponding to each temperature measurement point group to be determined is the same as the total number of the test error values, and they are all arranged according to time points.

[0067] S350: Based on several of the test error values, the temperature measurement point group to be determined, and several of the prediction error values corresponding to the temperature measurement point group to be determined, calculate the thermal key point coefficient corresponding to the temperature measurement point group to be determined.

[0068] The formula for the thermal key point coefficient is:

[0069]

[0070] Among them, represents the thermal key point coefficient, represents the number of test error values, represents the number of temperature measurement points to be determined in the temperature measurement point group to be determined, , represents the th test error value, represents the predicted error value corresponding to the th test error value, represents the average value of a number of test error values.

[0071] Step S40: Compare a number of the thermal key point coefficients, and establish the temperature measurement point group to be determined corresponding to the largest thermal key point coefficient as the thermal-sensitive temperature measurement point group, and the thermal-sensitive temperature measurement point group includes a number of thermal-sensitive temperature measurement points;

[0072] Preferably, the thermal-sensitive temperature measurement point is the thermal-sensitive point. It can be understood that the total number of thermal-sensitive points is less than the total number of the initial temperature measurement points. In the monitoring of the actual working condition, only monitoring the thermal-sensitive temperature measurement points can effectively predict the thermal error of the milling machine. The thermal-sensitive temperature measurement points have been selected through quantitative analysis to obtain the optimal solution, which is beneficial to greatly improving the prediction accuracy of the thermal error, while saving the use cost of the sensors, reducing the amount of data collected and processed during the prediction process, and making the prediction more efficient.

[0073] Step S50: Obtain the historical monitoring data of the electric spindle, and construct an electric spindle state model based on the historical monitoring data;

[0074] It can be understood that the performance of the electric spindle degrades over time, such as aging and wear. Specifically, the degradation of the spindle system bearings and the loosening of the motor rotor will cause an increase in the rotational error and vibration, usually bringing errors in the micron level, and this kind of error has a negative impact on the numerically controlled gantry milling machine with high-precision requirements. Therefore, establishing the electric spindle state model is beneficial to evaluating the state of the electric spindle and predicting the error caused by the degradation, making the prediction of the machining accuracy of the numerically controlled gantry milling machine more comprehensive and accurate.

[0075] The historical monitoring data includes the historical radial runout and the historical vibration signals of the front and rear bearings of the electric spindle.

[0076] Preferably, the radial runout is obtained by collecting the center locus at 25 mm from the end of the electric spindle shaft through two mutually perpendicular displacement sensors, and the vibration signals of the front and rear bearings are collected by acceleration sensors installed on the outer shell at the bearing positions. The unit of the radial runout is micron, and the unit of the vibration amount in the vibration signal is g.

[0077] The formula of the spindle state model is as follows:

[0078]

[0079] Wherein, represents the state of the spindle at moment, represents the state of the spindle at moment, represents the diffusion coefficient, represents the standard Brownian motion in the healthy stage of the spindle, represents the initial moment when the spindle starts to work, represents the moment when the spindle changes from the healthy stage to the slow degradation stage, represents the state of the spindle at moment, represents the drift coefficient, represents the standard Brownian motion in the slow degradation stage of the spindle, represents the moment when the spindle changes from the slow degradation stage to the rapid degradation stage, represents the state of the spindle at moment, represents the standard Brownian motion in the rapid degradation stage of the spindle.

[0080] Preferably, the spindle state model is established based on the state space model. The change of the spindle state includes three stages, namely the healthy stage, the slow degradation stage and the rapid degradation stage. In the healthy stage, the spindle runs smoothly, and the vibration index of the spindle reflected by the degradation state is near a constant value. The vibration index is further reflected as the radial jump variable, etc. When the spindle is in the slow degradation stage, the spindle can still be used normally, and the vibration index of the spindle changes linearly and uniformly. When the spindle is in the rapid degradation stage, the vibration index will no longer change uniformly. The parameters in the spindle state model and the change points of each stage are obtained through multiple simulation experiments and the training of the historical monitoring data. Specifically, in this embodiment, the change point from the healthy stage to the slow degradation stage is at 41.5% of the total service life of the spindle, and the change point from the slow degradation stage to the rapid degradation stage is at 79% of the total service life of the spindle.

[0081] Step S60: Obtain a number of updated temperature values from several of the thermosensitive temperature measurement points, and calculate the basic prediction error based on the number of updated temperature values;

[0082] Preferably, the unit of the basic prediction error is micrometers. Understandably, the number of temperature measurement points to be monitored is reduced, and the thermal error can be predicted more efficiently. Moreover, the basic prediction error is beneficial for providing data analysis for error compensation.

[0083] The step S60 includes:

[0084] S610: Based on a number of the temperature measurement curves and the error curves corresponding to the thermosensitive temperature measurement points, taking a number of the initial temperature values as independent variables and the test error values as dependent variables, performing multiple linear regression to obtain an error linear regression model;

[0085] Preferably, the machining error that changes with temperature change is reflected by the temperature data of the thermosensitive points. Fitting is performed according to a number of the obtained temperature measurement curves and the error curves. Based on the multiple linear regression model and the discrete data sampled in large quantities from the curves, a number of regression coefficients and correction coefficients of the multiple linear regression model are obtained to obtain the error linear regression model. The total number of regression coefficients is greater than the total number of the thermosensitive temperature measurement points, and different thermosensitive temperature measurement points correspond to different regression coefficients.

[0086] S620: Input a number of the updated temperature values into the error linear regression model to obtain a basic prediction error.

[0087] Preferably, a number of the updated temperature values are used as independent variables, and the updated temperature values collected at different thermosensitive temperature measurement points are multiplied by the corresponding regression coefficients, and the basic prediction error is calculated according to the error linear regression model.

[0088] Step S70: Obtain the updated monitoring data of the motorized spindle, obtain a degradation prediction error based on the updated monitoring data and the motorized spindle state model, and obtain a final prediction error based on the basic prediction error and the degradation prediction error.

[0089] Preferably, the potential degradation process of the motorized spindle system state can be described by the motorized spindle state model. By establishing a linear observation equation, the observed data can be associated with the state of the motorized spindle, that is, the degradation-related indexes are associated with the motorized spindle state, that is, the vibration indexes reflected by the monitoring data are associated with the motorized spindle state. The observation equation obtains the observation state based on the observation matrix and the observation noise, and the observation matrix is obtained based on the updated monitoring data. Understandably, the final prediction error takes into account the thermal error and the degradation state of the motorized spindle, and the machining accuracy prediction of the CNC gantry milling machine is more comprehensive and accurate.

[0090] The step S70 includes:

[0091] S710: Perform Kalman filtering on the updated monitoring data to obtain the updated state of the motorized spindle;

[0092] Preferably, for the motorized spindle in the healthy stage, a Kalman filter based on a constant is used to filter out the observation noise. For the motorized spindle in the slow degradation stage, a Kalman filter based on a linear model is used. For the motorized spindle in the rapid degradation stage, a Kalman filter based on a non - linear model is used.

[0093] S720: Obtain the degradation prediction error based on the updated state of the motorized spindle and the motorized spindle state model.

[0094] Preferably, the degradation prediction error reflects the predicted radial jump amount on the motorized spindle.

[0095] Please refer to Figure 2 , the machining accuracy prediction system of the numerically controlled gantry milling machine described in the second embodiment of the present invention is applied to the machining accuracy prediction method of the numerically controlled gantry milling machine as described in the above - mentioned first embodiment. The system includes:

[0096] The first test module 10 is used to set a number of initial temperature measurement points on the milling machine, run the milling machine to the thermal equilibrium state, collect a number of initial temperature values through the initial temperature measurement points, and construct a temperature measurement curve based on the initial temperature values and the corresponding time;

[0097] The second test module 20 is used to collect a number of real - time displacements of the motorized spindle through sensors to obtain test error values, and construct an error curve based on the time corresponding to the real - time displacements and the test error values;

[0098] The determination module 30 is used to divide a number of the initial temperature measurement points into a number of temperature measurement point groups based on the similarity between a number of the temperature measurement curves, construct a number of to - be - determined temperature measurement point groups based on the number of the temperature measurement point groups, and calculate a number of thermal key point coefficients corresponding to the number of the to - be - determined temperature measurement point groups;

[0099] The determination module 30 includes:

[0100] The first unit is used to calculate the temperature average value of all the initial temperature values in the temperature measurement point group, select the maximum average value from a number of the temperature average values, establish the temperature measurement point group corresponding to the maximum average value as the first temperature measurement point group, and establish the remaining number of the temperature measurement point groups as a number of second temperature measurement point groups;

[0101] The second unit is used to reorganize a number of the initial temperature measurement points in a number of the second temperature measurement point groups into a number of transition temperature measurement point groups;

[0102] The third unit is used to form a temperature measurement point group to be determined with the first temperature measurement point group and the transition temperature measurement point group;

[0103] In the determination module 30, the temperature measurement point group to be determined includes a plurality of temperature measurement points to be determined;

[0104] The determination module 30 further includes:

[0105] The fourth unit is used to obtain a plurality of initial temperature values at the same moment as the test error value through a plurality of the temperature measurement points to be determined, and establish them as a plurality of temperature values to be regressed, and perform linear regression on the plurality of temperature values to be regressed to obtain a prediction error value, and the temperature measurement point group to be determined corresponds to a plurality of the prediction error values;

[0106] The fifth unit is used to calculate a thermal key point coefficient corresponding to the temperature measurement point group to be determined based on a plurality of the test error values, the temperature measurement point group to be determined, and a plurality of the prediction error values corresponding to the temperature measurement point group to be determined.

[0107] In the determination module 30, the formula for the thermal key point coefficient is:

[0108]

[0109] Wherein, represents the thermal key point coefficient, represents the number of test error values, represents the number of temperature measurement points to be determined in the temperature measurement point group to be determined, , represents the th test error value, represents the prediction error value corresponding to the th test error value, represents the average value of a plurality of test error values.

[0110] The determination module 40 is used to compare a plurality of the thermal key point coefficients, and establish the temperature measurement point group to be determined corresponding to the largest thermal key point coefficient as a thermal sensitive temperature measurement point group, and the thermal sensitive temperature measurement point group includes a plurality of thermal sensitive temperature measurement points;

[0111] The construction module 50 is used to obtain the historical monitoring data of the motorized spindle, and construct a motorized spindle state model based on the historical monitoring data;

[0112] In the construction module 50, the historical monitoring data includes the historical radial runout of the motorized spindle and the historical vibration signals of the front and rear bearings.

[0113] The formula for the motorized spindle state model is:

[0114]

[0115] Among them, represents the state of the motorized spindle at moment, represents the state of the motorized spindle at moment, represents the diffusion coefficient, represents the standard Brownian motion in the healthy stage of the motorized spindle, represents the initial moment when the motorized spindle works, represents the moment when the motorized spindle transitions from the healthy stage to the slow degradation stage, represents the state of the motorized spindle at moment, represents the drift coefficient, represents the standard Brownian motion in the slow degradation stage of the motorized spindle, represents the moment when the motorized spindle transitions from the slow degradation stage to the rapid degradation stage, represents the state of the motorized spindle at moment, represents the standard Brownian motion in the rapid degradation stage of the motorized spindle.

[0116] The first prediction module 60 is configured to obtain a plurality of updated temperature values from a plurality of the thermosensitive temperature measurement points, and calculate a basic prediction error based on the plurality of updated temperature values;

[0117] The first prediction module 60 includes:

[0118] The sixth unit is configured to perform multiple linear regression with a plurality of the temperature measurement curves and the error curves corresponding to the thermosensitive temperature measurement points, with the plurality of initial temperature values as independent variables and the test error values as dependent variables, to obtain an error linear regression model;

[0119] The seventh unit is configured to input the plurality of updated temperature values into the error linear regression model to obtain a basic prediction error.

[0120] The second prediction module 70 is configured to obtain updated monitoring data of the motorized spindle, obtain a degradation prediction error based on the updated monitoring data and the motorized spindle state model, and obtain a final prediction error based on the basic prediction error and the degradation prediction error.

[0121] The second prediction module 70 includes:

[0122] The eighth unit is configured to perform Kalman filtering on the updated monitoring data to obtain an updated state of the motorized spindle;

[0123] The ninth unit is configured to obtain a degradation prediction error based on the updated state of the motorized spindle and the motorized spindle state model.

[0124] The third embodiment of the present invention provides a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the machining accuracy prediction method of the numerically controlled gantry milling machine as described in the first embodiment above.

[0125] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0126] The above-described embodiments only represent several implementation manners of the present invention. The descriptions are relatively specific and detailed, but should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several deformations and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention should be subject to the appended claims.

Claims

1. A method for predicting the machining accuracy of a numerically controlled gantry milling machine, characterized in that, It includes the following steps: Set a number of initial temperature measurement points on the milling machine, run the milling machine to the thermal equilibrium state, collect a number of initial temperature values through the initial temperature measurement points, and construct a temperature measurement curve based on the initial temperature values and the corresponding time; Collect a number of real-time displacements of the electric spindle through sensors to obtain test error values, and construct an error curve based on the time corresponding to the real-time displacements and the test error values; Based on the similarity between a number of the temperature measurement curves, divide a number of the initial temperature measurement points into a number of temperature measurement point groups, construct a number of to-be-determined temperature measurement point groups based on the number of the temperature measurement point groups, and calculate a number of thermal key point coefficients corresponding to the number of the to-be-determined temperature measurement point groups; The step of constructing a number of to-be-determined temperature measurement point groups based on a number of the temperature measurement point groups includes: Calculate the temperature average value of all the initial temperature values in the temperature measurement point group, select the maximum average value from a number of the temperature average values, establish the temperature measurement point group corresponding to the maximum average value as the first temperature measurement point group, and establish the remaining number of the temperature measurement point groups as a number of second temperature measurement point groups; Recombine a number of the initial temperature measurement points in a number of the second temperature measurement point groups into a number of transitional temperature measurement point groups; The first temperature measurement point group and the transitional temperature measurement point group form a to-be-determined temperature measurement point group; The to-be-determined temperature measurement point group includes a number of to-be-determined temperature measurement points, and the step of calculating a number of thermal key point coefficients corresponding to a number of the to-be-determined temperature measurement point groups includes: Obtain a number of the initial temperature values at the same moment as the test error values through a number of the to-be-determined temperature measurement points and establish them as a number of to-be-regressed temperature values, perform linear regression on a number of the to-be-regressed temperature values to obtain a prediction error value, and the to-be-determined temperature measurement point group corresponds to a number of the prediction error values; Based on a number of the test error values, the to-be-determined temperature measurement point group and a number of the prediction error values corresponding to the to-be-determined temperature measurement point group, calculate the thermal key point coefficient corresponding to the to-be-determined temperature measurement point group; Compare a number of the thermal key point coefficients, and establish the to-be-determined temperature measurement point group corresponding to the largest thermal key point coefficient as the thermal-sensitive temperature measurement point group, and the thermal-sensitive temperature measurement point group includes a number of thermal-sensitive temperature measurement points; Obtain the historical monitoring data of the electric spindle, and construct an electric spindle state model based on the historical monitoring data; Obtain a number of updated temperature values from a number of the thermal-sensitive temperature measurement points, and calculate a basic prediction error based on a number of the updated temperature values; Obtain the updated monitoring data of the electric spindle, obtain a degradation prediction error based on the updated monitoring data and the electric spindle state model, and obtain a final prediction error based on the basic prediction error and the degradation prediction error.

2. The method for predicting the machining accuracy of a numerically controlled gantry milling machine according to claim 1, wherein, The formula for the thermal key point coefficient is: Among them, represents the thermal key point coefficient, represents the number of test error values, represents the number of temperature measurement points to be determined in the temperature measurement point group to be determined, , represents the th test error value, represents the predicted error value corresponding to the th test error value, represents the average value of a number of test error values.

3. The method for predicting the machining accuracy of a numerically controlled gantry milling machine according to claim 1, wherein The historical monitoring data includes the historical radial runout amount and the historical vibration signals of the front and rear bearings of the electric spindle.

4. The method for predicting the machining accuracy of a numerically controlled gantry milling machine according to claim 1, wherein The formula for the electric spindle state model is: Among them, represents the state of the motorized spindle at moment, represents the state of the motorized spindle at moment, represents the diffusion coefficient, represents the standard Brownian motion of the motorized spindle in the healthy stage, represents the initial moment when the motorized spindle works, represents the moment when the motorized spindle transitions from the healthy stage to the slow degradation stage, represents the state of the motorized spindle at moment, represents the drift coefficient, represents the standard Brownian motion of the motorized spindle in the slow degradation stage, represents the moment when the motorized spindle transitions from the slow degradation stage to the rapid degradation stage, represents the state of the motorized spindle at moment, represents the standard Brownian motion of the motorized spindle in the rapid degradation stage.

5. The method for predicting the machining accuracy of a numerically controlled gantry milling machine according to claim 1, wherein The step of calculating the basic prediction error based on a number of the updated temperature values includes: Based on the plurality of the temperature measurement curves and the error curves corresponding to the thermosensitive temperature measurement points, taking the plurality of the initial temperature values as independent variables and the test error values as dependent variables, performing multiple linear regression to obtain an error linear regression model; Input the plurality of the updated temperature values into the error linear regression model to obtain a basic prediction error.

6. The machining accuracy prediction method of the numerically controlled gantry milling machine according to claim 1, wherein The step of obtaining a degradation prediction error based on the updated monitoring data and the electric spindle state model includes: Performing Kalman filtering on the updated monitoring data to obtain an updated state of the electric spindle; Based on the updated state of the electric spindle and the electric spindle state model, obtaining a degradation prediction error.

7. A numerical control gantry milling machine machining accuracy prediction system, applied to the numerical control gantry milling machine machining accuracy prediction method according to any one of claims 1 to 6 above, characterized in that, The system includes: A first test module, configured to set a plurality of initial temperature measurement points on a milling machine, run the milling machine to a thermal equilibrium state, collect a plurality of initial temperature values through the initial temperature measurement points, and construct a temperature measurement curve based on the initial temperature values and the corresponding time; A second test module, configured to collect a plurality of real-time displacements of an electric spindle through a sensor to obtain a test error value, and construct an error curve based on the time corresponding to the real-time displacement and the test error value; A determination module, configured to divide the plurality of the initial temperature measurement points into a plurality of temperature measurement point groups based on the similarity between the plurality of the temperature measurement curves, construct a plurality of to-be-determined temperature measurement point groups based on the plurality of the temperature measurement point groups, and calculate a plurality of thermal key point coefficients corresponding to the plurality of the to-be-determined temperature measurement point groups; The step of constructing a plurality of to-be-determined temperature measurement point groups based on the plurality of the temperature measurement point groups includes: Calculating the temperature average value of all the initial temperature values in the temperature measurement point group, selecting the maximum average value from the plurality of the temperature average values, establishing the temperature measurement point group corresponding to the maximum average value as a first temperature measurement point group, and establishing the remaining plurality of the temperature measurement point groups as a plurality of second temperature measurement point groups; Recombining the plurality of the initial temperature measurement points in the plurality of the second temperature measurement point groups into a plurality of transition temperature measurement point groups; The first temperature measurement point group and the transition temperature measurement point group form a to-be-determined temperature measurement point group; The to-be-determined temperature measurement point group includes a plurality of to-be-determined temperature measurement points, and the step of calculating a plurality of thermal key point coefficients corresponding to the plurality of the to-be-determined temperature measurement point groups includes: Obtaining a plurality of the initial temperature values at the same moment as the test error value through the plurality of the to-be-determined temperature measurement points, and establishing them as a plurality of to-be-regressed temperature values, performing linear regression on the plurality of the to-be-regressed temperature values to obtain a prediction error value, and the to-be-determined temperature measurement point group corresponds to the plurality of the prediction error values; Based on the plurality of the test error values, the to-be-determined temperature measurement point groups, and the plurality of the prediction error values corresponding to the to-be-determined temperature measurement point groups, calculating a thermal key point coefficient corresponding to the to-be-determined temperature measurement point group; An establishment module, configured to compare the plurality of the thermal key point coefficients, and establish the to-be-determined temperature measurement point group corresponding to the largest thermal key point coefficient as a thermosensitive temperature measurement point group, and the thermosensitive temperature measurement point group includes a plurality of thermosensitive temperature measurement points; A building module, configured to obtain historical monitoring data of the motorized spindle, and construct a motorized spindle state model based on the historical monitoring data; A first prediction module, configured to obtain a plurality of updated temperature values from a plurality of the thermistor temperature measurement points, and calculate a basic prediction error based on the plurality of updated temperature values; A second prediction module, configured to obtain updated monitoring data of the motorized spindle, obtain a degradation prediction error based on the updated monitoring data and the motorized spindle state model, and obtain a final prediction error based on the basic prediction error and the degradation prediction error.

8. A computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, the method for predicting the machining accuracy of a numerically controlled gantry milling machine according to any one of claims 1 to 6 is implemented.

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